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Proceedings Papers

Performing Fourier Transform on a Velocity Profile From Atmospheric Turbulence Studies

Richard Adansi, Jose Terrazas, Arturo Rodriguez, V. M. Krushnarao Kotteda, Vinod Kumar, Aldo Rubio, Edgar Avalos

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A035, August 10–12, 2021

Paper No: FEDSM2021-65812

DOI: https://doi.org/10.1115/FEDSM2021-65812

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Topics: Fourier transforms, Turbulence

Proceedings Papers

Causal Inference Analysis to Find Relationships Found in Boundary-Layer Transition – Part I: Theoretical

Arturo Rodriguez, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Jorge A. Munoz, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A050, August 10–12, 2021

Paper No: FEDSM2021-61843

DOI: https://doi.org/10.1115/FEDSM2021-61843

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Topics: Boundary layers, Turbulence, Flow (Dynamics), Computational fluid dynamics, Fluctuations (Physics), Reynolds number, Vehicles, Defense industry, Design, Fluid dynamics

Proceedings Papers

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed

Julio Aguilar, Laura Sandoval, Arturo Rodriguez, Sanjay Shantha Kumar, Jose Terrazas, Richard Adansi, Vinod Kumar, Arturo Bronson

Proc. ASME. FEDSM2021, Volume 3: Fluid Mechanics; Micro and Nano Fluid Dynamics; Multiphase Flow, V003T08A020, August 10–12, 2021

Paper No: FEDSM2021-65794

DOI: https://doi.org/10.1115/FEDSM2021-65794

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Topics: Alloys, Artificial neural networks, Equilibrium (Physics), Fluid dynamics, High temperature, Metals, Porosity, Scanning electron microscopes, Temperature, Vehicles

Proceedings Papers

Three-Dimensional Two-Phase Flow Simulations of Water Braking Phenomena for High-Speed Test Track Sled

Jose Terrazas, Arturo Rodriguez, Vinod Kumar, Richard Adansi, V. M. Krushnarao Kotteda

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A009, August 10–12, 2021

Paper No: FEDSM2021-65799

DOI: https://doi.org/10.1115/FEDSM2021-65799

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Topics: Braking, Computational fluid dynamics, Engineering simulation, Multiphase flow, Simulation, Two-phase flow, Water, Brakes, Inflow, Vehicles

Proceedings Papers

Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning

Nicholas Dudu, Arturo Rodriguez, Gael Moran, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Christopher Harris, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A051, August 10–12, 2021

Paper No: FEDSM2021-65798

DOI: https://doi.org/10.1115/FEDSM2021-65798

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Topics: Fractals, Machine learning, Turbulence, Dimensions, Artificial neural networks, Flow (Dynamics), Large eddy simulation, Scalars, Shapes, Trains

Proceedings Papers

Aerodynamic Performance of Design for a CO 2 Dragster

Brandon Paez, Arturo Rodriguez, Nicholas Dudu, Jose Terrazas, Richard Adansi, V. M. Krushnarao Kotteda, Julio C. Aguilar, Vinod Kumar

Proc. ASME. FEDSM2021, Volume 1: Aerospace Engineering Division Joint Track; Computational Fluid Dynamics, V001T02A040, August 10–12, 2021

Paper No: FEDSM2021-65793

DOI: https://doi.org/10.1115/FEDSM2021-65793

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Topics: Carbon dioxide, Design, Fluid structure interaction

Proceedings Papers

Stochastic Analysis of LES Atmospheric Turbulence Solutions With Generative Machine Learning Models

Arturo Rodriguez, Carlos R. Cuellar, Luis F. Rodriguez, Armando Garcia, V. S. Rao Gudimetla, V. M. Krushnarao Kotteda, Jorge A. Munoz, Vinod Kumar

Proc. ASME. FEDSM2020, Volume 1: Fluid Applications and Systems; Fluid Measurement and Instrumentation, V001T01A001, July 13–15, 2020

Paper No: FEDSM2020-20127

DOI: https://doi.org/10.1115/FEDSM2020-20127

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Topics: Machine learning, Turbulence

Proceedings Papers

Parameter Sensitivity and Statistical Correlation Found in Atmospheric Turbulence Studies

Luis F. Rodriguez, Vinod Kumar, Arturo Rodriguez, V. M. Krushnarao Kotteda, V. S. Rao Gudimetla, Jorge A. Munoz

Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T03A041, July 13–15, 2020

Paper No: FEDSM2020-20254

DOI: https://doi.org/10.1115/FEDSM2020-20254

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Topics: Computational fluid dynamics, Turbulence

Proceedings Papers

Trilinos Solvers Scalability on a MFiX-Trilinos Framework Applied to Fluidized Bed Simulations

Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Jorge A. Munoz

Proc. ASME. FEDSM2020, Volume 2: Fluid Mechanics; Multiphase Flows, V002T04A033, July 13–15, 2020

Paper No: FEDSM2020-20250

DOI: https://doi.org/10.1115/FEDSM2020-20250

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Topics: Engineering simulation, Fluidized beds, Multiphase flow, Simulation, Computational fluid dynamics, FORTRAN, Linear systems, Algorithms, Architecture, Flow (Dynamics)

Proceedings Papers

Branch and Bound Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence

Luis F. Rodriguez, Vinod Kumar, Jose Espiritu, Arturo Bronson, Krushnarao Kotteda, Diego Lozano, Arturo Rodriguez

Proc. ASME. AJKFluids2019, Volume 2: Computational Fluid Dynamics, V002T02A080, July 28–August 1, 2019

Paper No: AJKFluids2019-5567

DOI: https://doi.org/10.1115/AJKFluids2019-5567

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Topics: Lasers, Turbulence, Machine learning, Diffraction, Laser beams, Phase (Wave motion), Waves

Proceedings Papers

Machine Learning Approach to Predict the Flow Rate for an Immiscible Two-Phase Flow at Pore Scale for Enhanced Oil Recovery Application

Arturo Rodriguez, V. M. Krushnarao Kotteda, Luis F. Rodriguez, Vinod Kumar, Arturo Schiaffino, Zackery R. Nieto

Proc. ASME. FEDSM2018, Volume 2: Development and Applications in Computational Fluid Dynamics; Industrial and Environmental Applications of Fluid Mechanics; Fluid Measurement and Instrumentation; Cavitation and Phase Change, V002T09A003, July 15–20, 2018

Paper No: FEDSM2018-83050

DOI: https://doi.org/10.1115/FEDSM2018-83050

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Topics: Flow (Dynamics), Machine learning, Tertiary petroleum recovery, Two-phase flow, Petroleum extraction, Pressure drop, Surface tension, Viscosity, Algorithms, Brain

Proceedings Papers

Utilization of Machine Learning to Predict the Surface Tension of Metals and Alloys

Zackery Nieto, V. M. Krushnarao Kotteda, Arturo Rodriguez, Sanjay Shantha Kumar, Vinod Kumar, Arturo Bronson

Proc. ASME. FEDSM2018, Volume 3: Fluid Machinery; Erosion, Slurry, Sedimentation; Experimental, Multiscale, and Numerical Methods for Multiphase Flows; Gas-Liquid, Gas-Solid, and Liquid-Solid Flows; Performance of Multiphase Flow Systems; Micro/Nano-Fluidics, V003T21A003, July 15–20, 2018

Paper No: FEDSM2018-83248

DOI: https://doi.org/10.1115/FEDSM2018-83248

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Topics: Alloys, Machine learning, Metals, Surface tension, Algorithms, Copper, Silver, Computers, Liquid alloys, Neural network models

Journal citation

NARROW

Performing Fourier Transform on a Velocity Profile From Atmospheric Turbulence Studies

Causal Inference Analysis to Find Relationships Found in Boundary-Layer Transition – Part I: Theoretical

A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed

Three-Dimensional Two-Phase Flow Simulations of Water Braking Phenomena for High-Speed Test Track Sled

Fractal and Convolutional Analysis for Deep Atmospheric Turbulence Using Machine Learning

Aerodynamic Performance of Design for a CO 2 Dragster

Stochastic Analysis of LES Atmospheric Turbulence Solutions With Generative Machine Learning Models

Parameter Sensitivity and Statistical Correlation Found in Atmospheric Turbulence Studies

Trilinos Solvers Scalability on a MFiX-Trilinos Framework Applied to Fluidized Bed Simulations

Branch and Bound Analysis to Characterize Phase Variations in Laser Propagation Through Deep Turbulence

Machine Learning Approach to Predict the Flow Rate for an Immiscible Two-Phase Flow at Pore Scale for Enhanced Oil Recovery Application

Utilization of Machine Learning to Predict the Surface Tension of Metals and Alloys

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